An Image Processing Language with Icon-Assisted Navigation
IEEE Transactions on Software Engineering - Annals of discrete mathematics, 24
The capacity of the Hopfield associative memory
IEEE Transactions on Information Theory
IEEE Transactions on Software Engineering
Adaptive pattern recognition and neural networks
Adaptive pattern recognition and neural networks
Stability of the random neural network model
Neural Computation
Neural networks for signal processing
Neural networks for signal processing
A Visual Language Compiler for Information Retrieval by Visual Reasoning
IEEE Transactions on Software Engineering
Recursive neural networks for associative memory
Recursive neural networks for associative memory
A spatial knowledge structure for image information systems using symbolic projections
ACM '86 Proceedings of 1986 ACM Fall joint computer conference
Guest Editor's Introduction: Image Databases
IEEE Transactions on Software Engineering
PICQUERY: A High Level Query Language for Pictorial Database Management
IEEE Transactions on Software Engineering
An Intelligent Image Database System
IEEE Transactions on Software Engineering
An initiative for a classified bibliography on G-networks
Performance Evaluation
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A technique is developed based on the use of a neural network model for performing information retrieval in a pictorial information system. The neural network provides autoassociative memory operation and allows the retrieval of stored symbolic images using erroneous or incomplete information as input. The network used is based on an adaptation of the random neural network model featuring positive and negative nodes and symmetrical behavior of positive and negative signals. The network architecture considered has hierarchical structure and allows two-level operation during learning and recall. An experimental software prototype, including an efficient graphical interface, has been implemented and tested. The performance of the system has been investigated through experiments under several schemes concerning storage and reconstruction of patterns. These schemes are either based on properties of the random network or constitute adaptations of known neural network techniques.